PhD Researcher in Medical AI | ML Engineer | Generative AI

I am a Ph.D. researcher in Computer Engineering at the University of South Florida, working on Medical AI with a focus on Machine Learning, Deep Learning, and Generative AI for real-world healthcare applications. I previously completed my M.S. in Data Science at Texas A&M University.

My work sits at the intersection of research and engineering — I design models, build reproducible pipelines, run rigorous evaluations, and deploy systems that move beyond notebooks. I am particularly interested in computer vision, multimodal learning, and scalable ML systems.

I approach problems using first-principles thinking, emphasizing clarity, measurable impact, and end-to-end ownership — from raw data to deployed models.

📬 Let’s connect — Feel free to reach out via email or LinkedIn

📌 Currently seeking ML / AI Research Internships (2025–2027)

• 🏥 Medical AI: Vision-based ML for healthcare imaging and analysis
• 🧠 Machine Learning: Classical ML, Deep Learning, model optimization
• 🖼️ Computer Vision: Image understanding, 3D reconstruction, multimodal vision
• 🧬 Generative AI: LLMs, diffusion models, representation learning
• ⚙️ ML Systems: Data pipelines, training workflows, evaluation & ablations
• ☁️ Deployment & Scale: Dockerized workflows, cloud execution (AWS, HPRC)
• 📊 Analytics: SQL-driven analysis, dashboards, reproducible notebooks

• 🏥 PhD research applying ML/DL and Generative AI to medical and healthcare domains
• 🔬 Advanced topics in LLMs, multimodal learning, and generative modeling
• 🛠️ End-to-end ML projects using Python, PyTorch, SQL, Docker, and AWS
• 📈 Experimentation, benchmarking, and performance evaluation of ML models
• 🤝 Open-source contributions and collaborative research in AI & Data Science

• 🏥 Medical AI & Healthcare Applications
• 🧩 Applied Machine Learning & Deep Learning
• 🖼️ Computer Vision & 3D Reconstruction
• 🧬 Multimodal AI (Vision–Language Models)
• 🧠 Natural Language Processing & LLMs
• 📊 Data Analytics, Visualization & Storytelling
• 📦 ML Model Deployment & Scalable Workflows

Core Programming & Version Control

ML / Deep Learning

Data, Databases & Analytics

MLOps, Deployment & Cloud

Development & Experimentation

• 🧠 First-Principles Thinking – Break problems down to fundamentals, then rebuild with intent
• 🚀 Build for Impact – Favor solutions that deliver measurable, real-world value
• 🧩 Clarity Over Cleverness – Readable code, explainable models, clean system design
• 🛠 End-to-End Ownership – From data ingestion and modeling to evaluation and deployment
• 🌱 Long-Term Thinking – Optimize for scalability, robustness, and sustainable growth

When I hit a dead end, I step back and re-derive the problem from first principles — more often than not, the solution appears faster than brute-forcing code. ☕✨